Impact of Second-Line Combination Treatment for Type 2 Diabetes Mellitus on Disease Control: A Population-Based Cohort Study.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2023-09-01 Epub Date: 2023-05-09 DOI:10.1007/s40801-023-00374-2
Dan Ouchi, Carles Vilaplana-Carnerero, Ramon Monfà, Maria Giner-Soriano, Ana Garcia-Sangenís, Ferran Torres, Rosa Morros
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Abstract

Background: Type 2 diabetes mellitus is a chronic disease affecting millions of people worldwide. Achieving and maintaining glycemic control is essential to prevent or delay complications and different strategies are available as second-line treatment options for patients with type 2 diabetes who do not achieve glycemic control with metformin monotherapy.

Objective: The aim of this work is to describe the impact of initiating a combination treatment to reduce glycated hemoglobin in patients with type 2 diabetes with insufficient glycemic control.

Methods: We included patients with a type 2 diabetes diagnosis between 2015 and 2020 at the Information System for Research in Primary Care (SIDIAP) database in Catalonia, Spain. The primary outcome was the time to glycated hemoglobin control (≤ 7%) during the first 720 days, expressed as the restricted mean survival time. Adjusted differences of the restricted mean survival time were compared to analyze the performance of each treatment versus the combination with a sulfonylurea. Adherence was calculated as the medication possession ratio using an algorithm to model treatment exposure.

Results: A total of 28,425 patients were analyzed. The most frequent combinations were those with sulfonylureas and dipeptidyl peptidase-4 inhibitors. All treatments reduced glycated hemoglobin and the restricted mean survival time for the sulfonylurea treatment was 455 (451-459) days although combinations with glucagon-like peptide-1 and insulin reached glycemic control earlier, - 126 days (- 152 to - 100, p < 0.001) and - 69 days (- 88 to - 50, p < 0.001), respectively. Adherence was high in all groups apart from the insulin combination and had a significant effect in reducing glycated hemoglobin except in sodium-glucose cotransporter type 2 inhibitors and insulin. Glucagon-like peptide-1 and sodium-glucose cotransporter type 2 inhibitors showed significant reductions in weight.

Conclusions: Patients achieved the glycated hemoglobin goal with second-line treatments. Glucagon-like peptide-1 and insulin combinations achieved the goal earlier than sulfonylurea combinations. Adherence significantly reduced the time to glycated hemoglobin control except for the combination with sodium-glucose cotransporter type 2 inhibitors.

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二线联合治疗2型糖尿病对疾病控制的影响:一项基于人群的队列研究。
背景:2型糖尿病是一种影响全球数百万人的慢性疾病。实现和维持血糖控制对于预防或延迟并发症至关重要,对于二甲双胍单药治疗无法实现血糖控制的2型糖尿病患者,可以采用不同的策略作为二线治疗选择。目的:本工作的目的是描述在血糖控制不足的2型糖尿病患者中,启动降低糖化血红蛋白的联合治疗的影响。方法:我们纳入了西班牙加泰罗尼亚初级保健研究信息系统(SIDIAP)数据库中2015年至2020年间诊断为2型糖尿病的患者。主要结果是糖化血红蛋白控制的时间(≤ 7%)。比较限制平均生存时间的调整后差异,以分析每种治疗与磺酰脲联合治疗的效果。使用一种算法对治疗暴露进行建模,将依从性计算为药物占有率。结果:共对28425例患者进行了分析。最常见的组合是与磺酰脲类和二肽基肽酶-4抑制剂的组合。尽管与胰高血糖素样肽-1和胰岛素的组合更早达到血糖控制,但所有治疗都降低了糖化血红蛋白- 126天(- 152至- 100,p<0.001)和- 69天(- 88至- 50,p<0.001)。除胰岛素组合外,所有组的粘附性都很高,除钠-葡萄糖协同转运蛋白2型抑制剂和胰岛素外,粘附性在降低糖化血红蛋白方面具有显著作用。胰高血糖素样肽-1和钠-葡萄糖协同转运蛋白2型抑制剂显示体重显著减轻。结论:患者通过二线治疗达到了糖化血红蛋白的目标。胰高血糖素样肽-1和胰岛素组合比磺酰脲组合更早实现这一目标。除了与钠-葡萄糖协同转运蛋白2型抑制剂联合使用外,粘附显著缩短了糖化血红蛋白控制的时间。
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来源期刊
Drugs - Real World Outcomes
Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-
CiteScore
3.60
自引率
5.00%
发文量
49
审稿时长
8 weeks
期刊介绍: Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.
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